Phased Array Radio System Navigation of Unmanned Aerial Vehicles

Abstract

While global navigation satellite system (GNSS) provides accurate positioning and wide coverage for unmanned aerial vehicle (UAV) navigation, it is prone to threats like jamming and spoofing because of weak signal strength. Phased array radio system (PARS) emerge as a promising alternative or backup system, offering higher signal-to-noise ratio (SNR), narrow beam-directed communication, and robust encryption to counter these vulnerabilities. This thesis focuses on refining navigation techniques for UAVs independent or complementary to GNSS through the application of PARS. Our investigation centers on three main objectives: developing a calibration algorithm for accurately estimating the orientation of PARSs ground antennas, devising strategies to lessen the impact of multipath errors in vertical measurements from PARS, and creating a GNSS jamming detection algorithm for automatic handover or switching between GNSS- and PARS-aided inertial navigation system (INS). The initial segment of our research introduces a calibration algorithm for PARS ground antennas. Obtaining the precise estimate of the PARS ground antennas orientation is critical for PARS-based positioning, as the UAV position is measured with respect to the PARS ground antenna position and in the local PARS coordinate frame. Since the error in the estimation of the ground antenna orientation induces more error as the range between the UAV and the ground antenna becomes longer, the calibration algorithm is essential to achieve long-distance beyond-bine-of-sight (BLOS) flight. The calibration algorithm is based on multiplicative extended Kalman filter (MEKF) which estimates the ground antenna orientation using PARS and GNSS measurements, and enables in-flight calibration whenever reliable GNSS measurements are available. We evaluated the effectiveness of this algorithm, and the results underscored the algorithm’s capacity to significantly improve the positioning accuracy of UAVs by ensuring that the orientation of PARS antennas is pinpointed with a high degree of accuracy. Furthermore, we tackle the persistent issue of noise in PARS vertical measurements. Accurate vertical positioning is needed for the optimal operation of UAVs, and multipath interference, particularly over water, can significantly distort this data. Our research proposes methods utilizing barometric data to aid the vertical position or computing alternative elevation angle with incorporating the Earth’s curvature into consideration, aiming to mitigate these inaccuracies. The proposed solutions have shown potential in decreasing the errors caused by signal reflections, thereby enhancing the UAVs’ performance in various environmental conditions. Additionally, the threat of GNSS jamming to UAV navigation is addressed through the development of a jamming detection algorithm. Given the increasing prevalence of GNSS jamming and its potential to disrupt UAV navigation, this algorithm’s integration with the conventional PARS/barometer-aided INS represents a significant stride towards safeguarding UAV operations. This detection algorithm enables UAVs to identify jamming attempts in real-time, allowing for an adaptive response that maintains navigation accuracy even in compromised GNSS conditions. Throughout this study, we’ve tackled these challenges with a focus on careful exploration and understanding by combining theory with full-scale field experiments. By looking into how we can better calibrate PARS antennas, decrease noise in vertical measurements, and create a system to detect jamming, this thesis aims to contribute towards making UAV navigation more accurate and secure.